lsst.scarlet.lite.models.fit_psf.FittedPsfObservation Class Reference

Inheritance diagram for lsst.scarlet.lite.models.fit_psf.FittedPsfObservation:

Public Member Functions
	__init__ (self, np.ndarray|Image images, np.ndarray|Image variance, np.ndarray|Image weights, np.ndarray psfs, np.ndarray|None model_psf=None, np.ndarray|None noise_rms=None, Box|None bbox=None, tuple|None bands=None, int padding=3, str convolution_mode="fft", tuple[int, int]|None shape=None)
np.ndarray	grad_fit_kernel (self, np.ndarray input_grad, np.ndarray psf, np.ndarray model)
np.ndarray	prox_kernel (self, np.ndarray kernel)
np.ndarray	fitted_kernel (self)
	cached_kernel (self)
Image	convolve (self, Image image, str|None mode=None, bool grad=False)
	update (self, int it, np.ndarray input_grad, np.ndarray model)
None	parameterize (self, Callable parameterization)
Public Member Functions inherited from lsst.scarlet.lite.observation.Observation
	__init__ (self, np.ndarray|Image images, np.ndarray|Image variance, np.ndarray|Image weights, np.ndarray psfs, np.ndarray|None model_psf=None, np.ndarray|None noise_rms=None, Box|None bbox=None, tuple|None bands=None, int padding=3, str convolution_mode="fft")
tuple	bands (self)
Box	bbox (self)
float	log_likelihood (self, Image model)
Observation	__getitem__ (self, Any indices)
Observation	__copy__ (self)
Observation	__deepcopy__ (self, dict[int, Any] memo)
Observation	copy (self, bool deep=False)
tuple[int, int, int]	shape (self)
int	n_bands (self)
npt.DTypeLike	dtype (self)
tuple[int, int, int, int]	convolution_bounds (self)

Public Attributes
tuple	axes = (-2, -1)
Public Attributes inherited from lsst.scarlet.lite.observation.Observation
	images = images
	variance = _set_image_like(variance, bands, bbox)
	weights = _set_image_like(weights, bands, bbox)
	psfs = psfs
	mode = convolution_mode
	noise_rms = noise_rms
	model_psf = model_psf
	padding = padding
Fourier|None	diff_kernel = cast(Fourier, match_kernel(psfs, model_psf, padding=padding))
Fourier|None	grad_kernel = Fourier(diff_img[:, ::-1, ::-1])
	bands

Protected Attributes
	_fitted_kernel = parameter(cast(Fourier, self.diff_kernel).image)
Protected Attributes inherited from lsst.scarlet.lite.observation.Observation
tuple[int, int, int, int]|None	_convolution_bounds = None

Additional Inherited Members
Static Public Member Functions inherited from lsst.scarlet.lite.observation.Observation
Observation	empty (tuple[Any] bands, np.ndarray psfs, np.ndarray model_psf, Box bbox, npt.DTypeLike dtype)

Detailed Description

An observation that fits the PSF used to convolve the model.

Constructor & Destructor Documentation

__init__()

lsst.scarlet.lite.models.fit_psf.FittedPsfObservation.__init__	(		self,
		np.ndarray | Image	images,
		np.ndarray | Image	variance,
		np.ndarray | Image	weights,
		np.ndarray	psfs,
		np.ndarray | None	model_psf = None,
		np.ndarray | None	noise_rms = None,
		Box | None	bbox = None,
		tuple | None	bands = None,
		int	padding = 3,
		str	convolution_mode = "fft",
		tuple[int, int] | None	shape = None )

Initialize a `FitPsfObservation`

See `Observation` for a description of the parameters.

Member Function Documentation

convolve()

Image lsst.scarlet.lite.models.fit_psf.FittedPsfObservation.convolve	(		self,
		Image	image,
		str | None	mode = None,
		bool	grad = False )

Convolve the model into the observed seeing in each band.

Parameters
----------
image:
    The image to convolve
mode:
    The convolution mode to use.
    This should be "real" or "fft" or `None`,
    where `None` will use the default `convolution_mode`
    specified during init.
grad:
    Whether this is a backward gradient convolution
    (`grad==True`) or a pure convolution with the PSF.

Reimplemented from lsst.scarlet.lite.observation.Observation.

grad_fit_kernel()

np.ndarray lsst.scarlet.lite.models.fit_psf.FittedPsfObservation.grad_fit_kernel	(		self,
		np.ndarray	input_grad,
		np.ndarray	psf,
		np.ndarray	model )

Gradient of the loss wrt the PSF

This is just the cross correlation of the input gradient
with the model.

Parameters
----------
input_grad:
    The gradient of the loss wrt the model
psf:
    The PSF of the model.
model:
    The deconvolved model.

parameterize()

None lsst.scarlet.lite.models.fit_psf.FittedPsfObservation.parameterize	(		self,
		Callable	parameterization )

Convert the component parameter arrays into Parameter instances

Parameters
----------
parameterization: Callable
    A function to use to convert parameters of a given type into
    a `Parameter` in place. It should take a single argument that
    is the `Component` or `Source` that is to be parameterized.

update()

lsst.scarlet.lite.models.fit_psf.FittedPsfObservation.update	(		self,
		int	it,
		np.ndarray	input_grad,
		np.ndarray	model )

Update the PSF given the gradient of the loss

Parameters
----------
it: int
    The current iteration
input_grad: np.ndarray
    The gradient of the loss wrt the model
model: np.ndarray
    The deconvolved model.

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The documentation for this class was generated from the following file:

• python/lsst/scarlet/lite/models/fit_psf.py